Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "51" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 40 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 38 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459855 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 38.484088 | 2.682382 | 32.490026 | 1.216078 | 3.035994 | 2.659634 | 7.611310 | 3.671857 | 0.0403 | 0.7309 | 0.5526 | 1.140068 | 2.506430 |
| 2459854 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 39.558373 | 3.070744 | 25.112140 | 0.548252 | 4.113744 | 1.745831 | 12.194425 | 2.004085 | 0.0429 | 0.7489 | 0.5524 | 1.112758 | 2.379268 |
| 2459853 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459852 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 24.335003 | 5.314995 | 35.154569 | 2.247695 | 15.028492 | 2.857292 | 17.120810 | 3.137725 | 0.0451 | 0.8409 | 0.4830 | 1.148202 | 4.514513 |
| 2459851 | digital_ok | 100.00% | 89.15% | 0.00% | 0.00% | 100.00% | 0.00% | 25.843231 | 2.780386 | 35.876608 | 1.251025 | 21.220484 | 0.978896 | 22.782487 | 3.692281 | 0.1200 | 0.7609 | 0.4459 | 1.190593 | 3.659384 |
| 2459850 | digital_ok | 100.00% | 97.66% | 0.00% | 0.00% | 100.00% | 0.00% | 30.952027 | 2.737685 | 31.038196 | 0.822729 | 10.134753 | 1.564473 | 18.610144 | 3.482470 | 0.0717 | 0.7681 | 0.4692 | 1.124559 | 2.514897 |
| 2459849 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 36.038072 | 3.260774 | 62.145424 | 0.490749 | 7.015431 | 2.062584 | 16.681998 | 5.529135 | 0.0457 | 0.7614 | 0.4850 | 1.160655 | 3.356023 |
| 2459848 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 32.700189 | 3.310303 | 40.656614 | 0.015642 | 14.281149 | 1.247090 | 13.300799 | 2.398766 | 0.0453 | 0.7604 | 0.5076 | 1.190545 | 2.951385 |
| 2459847 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 35.358711 | 3.439402 | 37.986957 | 0.847267 | 21.770567 | 2.885305 | 8.057886 | 4.368840 | 0.0361 | 0.7010 | 0.4888 | 1.147040 | 2.942186 |
| 2459846 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 46.100427 | 3.326902 | 43.394723 | 0.450174 | 21.391674 | 2.512143 | 12.280990 | 2.752928 | 0.0384 | 0.6989 | 0.4390 | 1.156132 | 2.862393 |
| 2459845 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 39.098269 | 4.277114 | 52.687000 | -0.492953 | 191.794377 | 3.101555 | 381.776115 | 8.233860 | 0.0532 | 0.7597 | 0.5047 | 1.054132 | 4.884089 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.286074 | 25.978506 | 36.644175 | 19.376715 | 296.648337 | 115.131321 | 241.756403 | 90.324854 | 0.0215 | 0.0221 | 0.0002 | nan | nan |
| 2459843 | digital_ok | 100.00% | 100.00% | 0.66% | 0.00% | 100.00% | 0.00% | 37.249866 | 4.123398 | 25.112691 | -0.800046 | 335.236379 | 2.259519 | 1099.834408 | 11.942767 | 0.0427 | 0.7608 | 0.5032 | 1.210448 | 3.948356 |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 63.222308 | 47.740049 | 13.520923 | 13.483560 | 12.445347 | 7.549446 | 22.438779 | 15.767540 | 0.0215 | 0.0198 | 0.0014 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 16.132517 | 11.818477 | 32.205443 | 32.580060 | 4.179132 | 1.465074 | 27.731166 | 20.849117 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.306665 | 2.983365 | -0.466693 | 0.001290 | 0.137312 | 2.530931 | -0.166060 | 0.846276 | 0.7638 | 0.7174 | 0.3922 | 1.123619 | 0.973138 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0310 | 0.0317 | 0.0010 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.107961 | 1.033424 | 1.811337 | -0.551988 | 3.241200 | 4.087965 | 5.819019 | 10.177399 | 0.0309 | 0.0311 | 0.0010 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.650057 | 8.653448 | 4.191132 | 3.878386 | 8.487733 | 6.679263 | 16.121548 | 16.089101 | 0.0252 | 0.0241 | 0.0007 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.065912 | 3.811513 | -0.751726 | 0.252051 | -0.228549 | 2.350991 | 0.111361 | 0.979445 | 0.8112 | 0.5543 | 0.5624 | 1.760596 | 1.570901 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 16.627645 | 12.636503 | 34.817276 | 35.523997 | 3.711488 | 3.050134 | 21.700761 | 14.776034 | 0.0214 | 0.0200 | 0.0009 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.161605 | 3.687130 | -0.992245 | -0.268991 | 1.339536 | 0.982120 | 0.220567 | 2.203217 | 0.8107 | 0.5698 | 0.5376 | 1.706730 | 1.403208 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.354073 | 5.339917 | -0.423978 | -0.611432 | 0.361023 | 1.751567 | 1.387081 | 4.319885 | 0.7690 | 0.6835 | 0.3942 | 20.661594 | 18.035679 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.110460 | 2.881711 | -0.686740 | -0.657673 | 0.724490 | -0.923079 | -0.304344 | 2.850698 | 0.8069 | 0.5743 | 0.5248 | 1.666283 | 1.431507 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.987450 | 3.515104 | -0.694734 | -0.569426 | 0.211722 | 0.913428 | -0.818865 | 3.070317 | 0.7715 | 0.6888 | 0.3944 | 1.586087 | 1.384575 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 2.63% | 0.00% | 1.478230 | 2.684612 | -0.715361 | -0.405165 | 2.566452 | 0.100709 | 0.491489 | 1.145510 | 0.8032 | 0.5872 | 0.5054 | 1.350237 | 1.030595 |
| 2459825 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459824 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.578706 | 2.272366 | -0.212601 | 0.282034 | 0.317181 | 0.619651 | 0.117799 | 2.763499 | 0.7418 | 0.7492 | 0.3453 | 1.729065 | 1.836192 |
| 2459823 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.586236 | 1.161557 | -0.608498 | -0.195303 | 0.089422 | 1.211805 | -0.201392 | 2.243395 | 0.7787 | 0.6625 | 0.4390 | 2.130212 | 2.051685 |
| 2459822 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.558099 | 2.270638 | -0.778862 | -0.554454 | 1.305058 | -0.942390 | -0.284045 | 4.049469 | 0.8062 | 0.6223 | 0.4929 | 5.067919 | 4.846087 |
| 2459821 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 364.913230 | 365.141779 | inf | inf | 685.086276 | 657.557950 | 1369.585431 | 1384.492320 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.960166 | 3.641697 | -0.810700 | -0.659443 | 1.228890 | -0.097778 | -0.174457 | 1.036263 | 0.7825 | 0.7093 | 0.4057 | 1.621015 | 1.492087 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.753752 | 1.835069 | -0.876639 | -0.782278 | 1.111293 | -0.241855 | -0.341777 | 1.040572 | 0.8103 | 0.6715 | 0.4916 | 1.850650 | 1.692432 |
| 2459816 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.819192 | 2.132550 | -0.959660 | -0.118302 | 2.117951 | 0.649659 | 0.533906 | 4.909612 | 0.8041 | 0.6842 | 0.4977 | 3.729318 | 3.357534 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.995677 | 6.431999 | -0.831876 | -0.654601 | 4.744317 | 1.524870 | 1.108258 | 4.764817 | 0.8058 | 0.7563 | 0.3777 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Shape | 38.484088 | 2.682382 | 38.484088 | 1.216078 | 32.490026 | 2.659634 | 3.035994 | 3.671857 | 7.611310 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Shape | 39.558373 | 3.070744 | 39.558373 | 0.548252 | 25.112140 | 1.745831 | 4.113744 | 2.004085 | 12.194425 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Power | 35.154569 | 24.335003 | 5.314995 | 35.154569 | 2.247695 | 15.028492 | 2.857292 | 17.120810 | 3.137725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Power | 35.876608 | 25.843231 | 2.780386 | 35.876608 | 1.251025 | 21.220484 | 0.978896 | 22.782487 | 3.692281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Power | 31.038196 | 30.952027 | 2.737685 | 31.038196 | 0.822729 | 10.134753 | 1.564473 | 18.610144 | 3.482470 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Power | 62.145424 | 36.038072 | 3.260774 | 62.145424 | 0.490749 | 7.015431 | 2.062584 | 16.681998 | 5.529135 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Power | 40.656614 | 3.310303 | 32.700189 | 0.015642 | 40.656614 | 1.247090 | 14.281149 | 2.398766 | 13.300799 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Power | 37.986957 | 3.439402 | 35.358711 | 0.847267 | 37.986957 | 2.885305 | 21.770567 | 4.368840 | 8.057886 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Shape | 46.100427 | 46.100427 | 3.326902 | 43.394723 | 0.450174 | 21.391674 | 2.512143 | 12.280990 | 2.752928 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Temporal Discontinuties | 381.776115 | 4.277114 | 39.098269 | -0.492953 | 52.687000 | 3.101555 | 191.794377 | 8.233860 | 381.776115 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Temporal Variability | 296.648337 | 12.286074 | 25.978506 | 36.644175 | 19.376715 | 296.648337 | 115.131321 | 241.756403 | 90.324854 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Temporal Discontinuties | 1099.834408 | 4.123398 | 37.249866 | -0.800046 | 25.112691 | 2.259519 | 335.236379 | 11.942767 | 1099.834408 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Shape | 63.222308 | 63.222308 | 47.740049 | 13.520923 | 13.483560 | 12.445347 | 7.549446 | 22.438779 | 15.767540 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Power | 32.580060 | 11.818477 | 16.132517 | 32.580060 | 32.205443 | 1.465074 | 4.179132 | 20.849117 | 27.731166 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 2.983365 | 2.983365 | 1.306665 | 0.001290 | -0.466693 | 2.530931 | 0.137312 | 0.846276 | -0.166060 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Temporal Discontinuties | 10.177399 | 1.033424 | -0.107961 | -0.551988 | 1.811337 | 4.087965 | 3.241200 | 10.177399 | 5.819019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | ee Temporal Discontinuties | 16.121548 | 8.653448 | 9.650057 | 3.878386 | 4.191132 | 6.679263 | 8.487733 | 16.089101 | 16.121548 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 3.811513 | 2.065912 | 3.811513 | -0.751726 | 0.252051 | -0.228549 | 2.350991 | 0.111361 | 0.979445 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Power | 35.523997 | 16.627645 | 12.636503 | 34.817276 | 35.523997 | 3.711488 | 3.050134 | 21.700761 | 14.776034 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 3.687130 | 2.161605 | 3.687130 | -0.992245 | -0.268991 | 1.339536 | 0.982120 | 0.220567 | 2.203217 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 5.339917 | 5.339917 | 3.354073 | -0.611432 | -0.423978 | 1.751567 | 0.361023 | 4.319885 | 1.387081 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 2.881711 | 2.881711 | 2.110460 | -0.657673 | -0.686740 | -0.923079 | 0.724490 | 2.850698 | -0.304344 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 3.515104 | 1.987450 | 3.515104 | -0.694734 | -0.569426 | 0.211722 | 0.913428 | -0.818865 | 3.070317 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 2.684612 | 2.684612 | 1.478230 | -0.405165 | -0.715361 | 0.100709 | 2.566452 | 1.145510 | 0.491489 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Temporal Discontinuties | 2.763499 | 0.578706 | 2.272366 | -0.212601 | 0.282034 | 0.317181 | 0.619651 | 0.117799 | 2.763499 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Temporal Discontinuties | 2.243395 | 1.161557 | 1.586236 | -0.195303 | -0.608498 | 1.211805 | 0.089422 | 2.243395 | -0.201392 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Temporal Discontinuties | 4.049469 | 1.558099 | 2.270638 | -0.778862 | -0.554454 | 1.305058 | -0.942390 | -0.284045 | 4.049469 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Power | inf | 365.141779 | 364.913230 | inf | inf | 657.557950 | 685.086276 | 1384.492320 | 1369.585431 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 3.641697 | 1.960166 | 3.641697 | -0.810700 | -0.659443 | 1.228890 | -0.097778 | -0.174457 | 1.036263 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 1.835069 | 0.753752 | 1.835069 | -0.876639 | -0.782278 | 1.111293 | -0.241855 | -0.341777 | 1.040572 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Temporal Discontinuties | 4.909612 | 2.132550 | 0.819192 | -0.118302 | -0.959660 | 0.649659 | 2.117951 | 4.909612 | 0.533906 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | digital_ok | nn Shape | 6.431999 | 6.431999 | 2.995677 | -0.654601 | -0.831876 | 1.524870 | 4.744317 | 4.764817 | 1.108258 |